Facial-Recognition
Face Recognition Model using OpenCV and Deep Learning. First Face detection was performed, Used a pre-trained Caffe deep learning model imported using OpenCV to detect faces. This model detects and recognize the faces of five people known to me. After Detection face embeddings were extracted for each face using deep learning.The FaceNet deep learning model computes a 128-d embedding that quantifies the face. Trained a classification on the embeddings, and then finally recognize faces in both images and video streams with OpenCV.
Facial-Recognition
Face Recognition Model using OpenCV and Deep Learning.
Description
First Face detection was performed, Used a pre-trained Caffe deep learning model imported using OpenCV to detect faces. This model detects and recognize the faces of five people known to me. After Detection face embeddings were extracted from each face using deep learning.The FaceNet deep learning model computes a 128-d embedding that quantifies the face. Trained a face recognition model on the embeddings, and then finally recognize faces in both images and video streams with OpenCV.
Getting Started
Dependencies
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Jupyter Notebook required
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Python Libraries
- Imutils
- Tensorflow
- Keras
- Numpy
- Pickle
- cv2
- os
- Scikit-Learn
- Matplotlib
Installing
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Download Jupyter Notebook
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No further installation
Executing program
There are 4 phases of this program, run each of them.
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Extract embeddings from face dataset
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Train face recognition model
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Recognize faces with OpenCV
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Recognize faces in video streams
Help
Installing the libraries beforehand will solve most issues
Authors
Contributors names and contact info ex. @priyanshkedia04
Version History
- 0.2
- Documentation added
- See commit change or See release history
- 0.1
- Initial Release
License
GNU General Public License v3.0
Acknowledgments
Acknowledgements to be added